Moving Average
Nikolas Kellogg
- Q1 Import S&P500 for the last five years.
- Q2 Calculate 100-day and 200-day simple moving averages.
- Q3 Transform data to long form from wide form for graphing.
- Q4 Visualize data.
- Q5 List both bullish or bearish crossovers with dates and closing prices.
- Q6 If you had invested $1 million on the day of the first bullish crossover and followed the moving average strategy throughout the considered period, would you have more or less than $1 million today?
- Q7 Repeat Q2- Q6 for 50-day and 100-day simple moving average.
- Q2 Calculate 50-day and 100-day simple moving averages.
- Q3 Transform data to long form from wide form for graphing.
- Q4 Visualize data.
- Q5 List both bullish or bearish crossovers with dates and closing prices.
- Q6 If you had invested $1 million on the day of the first bullish crossover and followed the moving average strategy throughout the considered period, would you have more or less than $1 million today?
Q1 Import S&P500 for the last five years.
Hint: Copy and revise the importing part of the code from above.
library(tidyquant)
library(tidyverse)
# Conduct the trend analysis (moving average) for the Tesla stock.
# Import data
from = today() - years(5)
stock <- tq_get("^GSPC", get = "stock.prices", from = from)
stock
## # A tibble: 1,258 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2014-03-19 1872. 1874. 1850. 1861. 3289210000 1861.
## 2 2014-03-20 1860. 1873. 1855. 1872. 3327540000 1872.
## 3 2014-03-21 1875. 1884. 1863. 1867. 5270710000 1867.
## 4 2014-03-24 1868. 1873. 1850. 1857. 3409000000 1857.
## 5 2014-03-25 1859. 1872. 1856. 1866. 3200560000 1866.
## 6 2014-03-26 1867. 1876. 1853. 1853. 3480850000 1853.
## 7 2014-03-27 1852. 1856. 1842. 1849. 3733430000 1849.
## 8 2014-03-28 1850. 1867. 1850. 1858. 2955520000 1858.
## 9 2014-03-31 1859. 1875. 1859. 1872. 3274300000 1872.
## 10 2014-04-01 1874. 1886. 1874. 1886. 3336190000 1886.
## # ... with 1,248 more rowsQ2 Calculate 100-day and 200-day simple moving averages.
Hint: Copy and revise the moving average part of the code from above.
# Add the 100-day and 200-day simple moving average by passing close prices
stock <-
stock %>%
tq_mutate(select = close, mutate_fun = SMA, n = 100) %>%
rename(SMA.short = SMA) %>%
tq_mutate(select = close, mutate_fun = SMA, n = 200) %>%
rename(SMA.long = SMA)
stock
## # A tibble: 1,258 x 9
## date open high low close volume adjusted SMA.short SMA.long
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2014-03-19 1872. 1874. 1850. 1861. 3.29e9 1861. NA NA
## 2 2014-03-20 1860. 1873. 1855. 1872. 3.33e9 1872. NA NA
## 3 2014-03-21 1875. 1884. 1863. 1867. 5.27e9 1867. NA NA
## 4 2014-03-24 1868. 1873. 1850. 1857. 3.41e9 1857. NA NA
## 5 2014-03-25 1859. 1872. 1856. 1866. 3.20e9 1866. NA NA
## 6 2014-03-26 1867. 1876. 1853. 1853. 3.48e9 1853. NA NA
## 7 2014-03-27 1852. 1856. 1842. 1849. 3.73e9 1849. NA NA
## 8 2014-03-28 1850. 1867. 1850. 1858. 2.96e9 1858. NA NA
## 9 2014-03-31 1859. 1875. 1859. 1872. 3.27e9 1872. NA NA
## 10 2014-04-01 1874. 1886. 1874. 1886. 3.34e9 1886. NA NA
## # ... with 1,248 more rowsQ3 Transform data to long form from wide form for graphing.
Hint: Copy and revise the transformation part of the code from above.
# Transform to long form to wide form for graphing
stock_long <-
stock %>%
select(date, close, SMA.short, SMA.long) %>%
gather(key = type, value = price, close:SMA.long)
stock_long
## # A tibble: 3,774 x 3
## date type price
## <date> <chr> <dbl>
## 1 2014-03-19 close 1861.
## 2 2014-03-20 close 1872.
## 3 2014-03-21 close 1867.
## 4 2014-03-24 close 1857.
## 5 2014-03-25 close 1866.
## 6 2014-03-26 close 1853.
## 7 2014-03-27 close 1849.
## 8 2014-03-28 close 1858.
## 9 2014-03-31 close 1872.
## 10 2014-04-01 close 1886.
## # ... with 3,764 more rowsQ4 Visualize data.
Hint: Copy and revise the visualization part of the code from above.
# Visualize
stock_long %>%
ggplot(aes(x = date, y = price, col = type)) +
geom_line() +
theme(legend.position="bottom") +
labs(title = "Simple Moving Averages are a Breeze with tidyquant",
x = NULL,
y = "Stock Prices")
Q5 List both bullish or bearish crossovers with dates and closing prices.
- A bullish crossover if the 100-day moving average cross above the 200-day moving average.
Bullish crossover happens on September 21st to 22nd in 2015, short = 2068.185 and long = 2068.048, then changes over to short = 2066.757 and long = 2067.403
- A bearish crossover if the 100-day moving average cross below the 200-day moving average.
Bearish crossover happens on June 9th to 10th in 2016, short = 2013.078 and long = 2013.423, then changes over to short = 2015.226 and long = 2014.565
Q6 If you had invested $1 million on the day of the first bullish crossover and followed the moving average strategy throughout the considered period, would you have more or less than $1 million today?
You would have a lot more than 1 million simply because the price of S&P has went from about 2100 at the time to now being over 2750 per share. More specifically you would have reinvested in mid 2016 which would have shown huge gains for the next 2 year until the next bullish crossover.
Q7 Repeat Q2- Q6 for 50-day and 100-day simple moving average.
Q2 Calculate 50-day and 100-day simple moving averages.
Hint: Copy and revise the moving average part of the code from above.
# Add the 50-day and 100-day simple moving average by passing close prices
stock <-
stock %>%
tq_mutate(select = close, mutate_fun = SMA, n = 50) %>%
rename(SMA.50 = SMA) %>%
tq_mutate(select = close, mutate_fun = SMA, n = 100) %>%
rename(SMA.100 = SMA)
stock
## # A tibble: 1,258 x 11
## date open high low close volume adjusted SMA.short SMA.long
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2014-03-19 1872. 1874. 1850. 1861. 3.29e9 1861. NA NA
## 2 2014-03-20 1860. 1873. 1855. 1872. 3.33e9 1872. NA NA
## 3 2014-03-21 1875. 1884. 1863. 1867. 5.27e9 1867. NA NA
## 4 2014-03-24 1868. 1873. 1850. 1857. 3.41e9 1857. NA NA
## 5 2014-03-25 1859. 1872. 1856. 1866. 3.20e9 1866. NA NA
## 6 2014-03-26 1867. 1876. 1853. 1853. 3.48e9 1853. NA NA
## 7 2014-03-27 1852. 1856. 1842. 1849. 3.73e9 1849. NA NA
## 8 2014-03-28 1850. 1867. 1850. 1858. 2.96e9 1858. NA NA
## 9 2014-03-31 1859. 1875. 1859. 1872. 3.27e9 1872. NA NA
## 10 2014-04-01 1874. 1886. 1874. 1886. 3.34e9 1886. NA NA
## # ... with 1,248 more rows, and 2 more variables: SMA.50 <dbl>,
## # SMA.100 <dbl>Q3 Transform data to long form from wide form for graphing.
Hint: Copy and revise the transformation part of the code from above.
# Transform to long form to wide form for graphing
stock_long <-
stock %>%
select(date, close, SMA.50, SMA.100) %>%
gather(key = type, value = price, close:SMA.100)
stock_long
## # A tibble: 3,774 x 3
## date type price
## <date> <chr> <dbl>
## 1 2014-03-19 close 1861.
## 2 2014-03-20 close 1872.
## 3 2014-03-21 close 1867.
## 4 2014-03-24 close 1857.
## 5 2014-03-25 close 1866.
## 6 2014-03-26 close 1853.
## 7 2014-03-27 close 1849.
## 8 2014-03-28 close 1858.
## 9 2014-03-31 close 1872.
## 10 2014-04-01 close 1886.
## # ... with 3,764 more rowsQ4 Visualize data.
Hint: Copy and revise the visualization part of the code from above.
# Visualize
stock_long %>%
ggplot(aes(x = date, y = price, col = type)) +
geom_line() +
theme(legend.position="bottom") +
labs(title = "Simple Moving Averages are a Breeze with tidyquant",
x = NULL,
y = "Stock Prices")
Q5 List both bullish or bearish crossovers with dates and closing prices.
- A bullish crossover if the 100-day moving average cross above the 200-day moving average.
Bullish crossover happens on August 5th and 6th in 2015, 50 = 2097.965 and 100 = 2097.582, then changes over to 50 = 2097.167 and 100 = 2097.606
- A bearish crossover if the 100-day moving average cross below the 200-day moving average.
Bearish crossover happens on December 6th and 7th in 2015, 50 = 2033.836 and 100 = 2035.227, then changes over to 50 = 2036.572 and 100 = 2035.026
Q6 If you had invested $1 million on the day of the first bullish crossover and followed the moving average strategy throughout the considered period, would you have more or less than $1 million today?
You would have a lot more than 1 million simply because the price of S&P has went from about 2100 at the time to now being over 2750 per share. More specifically you would have reinvested in throughout 2015, 2016, 2017, and 2018, which would have shown huge gains for the next 4 year until the next bullish crossover.